Spatial data clustering in independent mobile environment

• Autorzy: Gajewski B. , Martyn T.
• Języki publikacji: EN

Abstrakty

EN

Most geolocation applications for mobile devices assume a constant connection with the network and high computational power nodes. However, with ever-developing devices it now becomes possible to establish peer-to-peer networks in case when the network can be unreachable due to special circumstances (like conflicts or natural disasters). In this paper, a method for clustering spatial data in mobile environment is discussed. A simple solution based on OPTICS algorithm with lexical distance is proposed for grouping the observations.

Słowa kluczowe

EN

peer-to-peer; data clustering; OPTICS; mobile; lexical distance

• Wydawca: Wydawnictwo PAK
• Czasopismo: Measurement Automation Monitoring
• Rocznik: 2016
• Tom: Vol. 62, No. 5
• Strony: 163--165
• Opis fizyczny: Bibliogr. 13 poz., rys., tab., wzory

Twórcy

autor

Gajewski B.

• Warsaw University of Technology, Institute of Computer Science 15/19 Nowowiejska St., 00-665 Warsaw, Poland

autor

Martyn T.

• Warsaw University of Technology, Institute of Computer Science 15/19 Nowowiejska St., 00-665 Warsaw, Poland

Bibliografia

• [1] Pence Harry E.: Smartphones, smart objects, and augmented reality. The Reference Librarian 52.1-2 (2010), pp. 136-145.
• [2] Ester Martin, et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. Vol. 96, No. 34, 1996.
• [3] Wang Wei, Jiong Yang, and Muntz Richard: STING: A statistical information grid approach to spatial data mining. VLDB, Vol. 97, 1997.
• [4] Ankerst Mihael, et al.: OPTICS: ordering points to identify the clustering structure. ACM Sigmod Record. Vol. 28, No. 2, ACM, 1999.
• [5] Altman N. S.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46 (3): 175–185, 1992.
• [6] Koperski K., and Jiawei Han: Data mining methods for the analysis of large geographic databases. Proc. of 10th Annual Conf. on GIS, Vancouver, BC, 1996.
• [7] Cichosz P.: Data Mining Algorithms: Explained Using R. John Wiley & Sons, 2014.
• [8] Chmielewski M., Gałka A.: Automated mapping JC3IEDM data in tactical symbology standards for Common Operational Picture services. Proceedings of the Military Communications and Information Systems Conference MCC. 2009.
• [9] Levenshtein V.I.: Binary codes capable of correcting deletions, insertions, and reversals." Soviet physics doklady, Vol. 10, No. 8, 1966.
• [10] Navarro G.: A guided tour to approximate string matching. ACM computing surveys (CSUR) 33.1 (2001): 31-88.
• [11] mCOP application homepage at http://uranus.wat.edu.pl:8808/ wiki/index.php/MCOP
• [12] Gajewski B. K., and Martyn T.: Smart mobile P2P communication optimization for close range by an automatic interface switch. Measurement Automation and Monitoring, vol. 61, no. 07, 2015, pp. 317–319.
• [13] ELKI: Environment for Developing KDD-Applications Supported by Index-Structures Open Source project. http://elki.dbs.ifi.lmu.de/